Federated Learning

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Overview

Federated learning studies how multiple devices or institutions can train models collaboratively while keeping data local, for example across hospitals, phones, or companies. A common approach, FedAvg, aggregates local model updates; related methods use personalization or client clustering, split learning, model compression, and lightweight adapters such as LoRA. Differential privacy and secure aggregation can limit exposure of updates, while robust aggregation and client-selection methods address malicious participants and uneven data or resources. The central challenge is balancing accuracy and fairness across heterogeneous clients with privacy, security, communication cost, and reliable evaluation; keeping data local alone does not guarantee privacy or strong performance.

Latest papers

CardsList

961 papers112 papers in the last four weeks, up 1% on the four weeks before. 0.6% of all new papers.

  1. Federated Independent Component Analysis via Spectral Alignment and Robust Aggregation

    May 26, 2025Xin Bing, Dian Jin, Yuqian ZhangFederated Learning AggregationIndependent Component Analysis

    University of Toronto · National University of Singapore · Rutgers University

  2. Federated Learning: From Theory to Practice

    May 25, 2025A. JungFederated Learning

    Espoo, Finland: Aalto University

  3. Incentivize Contribution and Learn Parameters Too: Federated Learning with Strategic Data Owners

    May 17, 2025Drashthi Doshi, Avishek Ghosh, Swaprava NathIncentive Mechanisms for Federated LearningFederated Learning

    IIT Bombay

  4. FedHQ: Hybrid Runtime Quantization for Federated Learning

    May 17, 2025Zihao Zheng, Ziyao Wang, Jiawei Yang +7Federated LearningAdaptive Quantization

    Peking University · University of Maryland

  5. Heterogeneity-Aware Client Sampling for Optimal and Efficient Federated Learning

    May 16, 2025Shudi Weng, Chao Ren, Ming Xiao +1Federated LearningHeterogeneous Federated Learning

    Division of Information Science and Engineering, School of Electrical Engineering and Computer Science (EECS), KTH Royal Institute of Technology, 11428 Stockholm, Sweden

  6. Data-Driven Laplacian-Constrained Personalized Federated Learning

    May 12, 2025Samuel Erickson, Mikael JohanssonFederated LearningNon-IID Federated Learning

    Division of Decision and Control Systems, KTH Royal Institute of Technology, Stockholm, Sweden

  7. Remote Rowhammer Attack using Adversarial Observations on Federated Learning Clients

    May 9, 2025Jinsheng Yuan, Yuhang Hao, Weisi Guo +2Federated Learning

    Faculty of Engineering & Applied Sciences, Cranfield University, UK. · Queen’s University Belfast.

  8. CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection

    Apr 2, 2025Diego Cajaraville-Aboy, Marta Moure-Garrido, Carlos Beis-Penedo +5Network Intrusion DetectionEncrypted Network Traffic Analysis

    atlanTTic Research Center – ICLAB – Universidade de Vigo, Vigo, 36310, Spain · Department of Telematic Engineering, Universidad Carlos III de Madrid, Spain

  9. Unified Robust and Convergent Over-The-Air Federated Learning under Channel Noise and Heterogeneity

    Mar 25, 2025Zubair Shaban, Nazreen Shah, Ranjitha PrasadFederated LearningOver-the-Air Federated Learning

    ECE dept., IIIT Delhi, New Delhi.

  10. Asynchronous Federated Continual Segmentation with Evolving Clients and Label Spaces

    Mar 19, 2025Can Peng, Qianhui Men, Pramit Saha +5Federated Learning AggregationFederated Continual Learning

    University of Oxford, Oxford, United Kingdom · University of Bristol, Bristol, United Kingdom

  11. FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in Federated Learning

    Feb 23, 2025Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad SaifData Poisoning AttacksFederated Learning

    Department of Electrical and Computer Engineering, University of Windsor, Windsor ON, Canada · Faculty of Computer Science, University of New Brunswick, Fredericton NB, Canada

  12. Correcting Before Adapting: Gradient Correction for Federated Adaptive Optimization

    Feb 4, 2025Evan Chen, Shiqiang Wang, Dong-Jun Han +2Federated LearningHeterogeneous Federated Learning

    Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN · IBM T. J. Watson Research Center, Yorktown Heights, NY, USA · Department of Computer Science and Engineering, Yonsei University, Seoul, Korea

  13. GDBR: Label Recovery Attack Against Partial Gradient Encryption in Federated Learning

    Dec 17, 2024Rui Zhang, Ka-Ho ChowGradient Inversion AttacksAdversarial Attacks

    HKU Musketeers Foundation Institute of Data Science · The University of Hong Kong · Hong Kong SAR, China +1

  14. How Can Incentives and Cut Layer Selection Influence Data Contribution in Split Federated Learning?

    Dec 10, 2024Joohyung Lee, Jungchan Cho, Wonjun Lee +2Mechanism DesignSplit Federated Learning

    School of Computing, Gachon University, Seongnam 13120, Rep. of Korea · School of Cybersecurity, Korea University, Seoul, Rep. of Korea · Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544 USA

  15. A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applications

    Dec 9, 2024Francesco Cremonesi, Lucia Innocenti, Sebastien Ourselin +3Communication-Efficient Distributed TrainingFederated Learning

    Epione Research Group, Inria Center of Universit´e Cˆote d’Azur, France · King’s College London, London, UK

  16. H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications

    Dec 9, 2024Jiechao Gao, Yuangang Li, Jie Wang +3Hierarchical Federated LearningInternet of Things

    University of Virginia Charlottesville, VA, USA

  17. Batch effects can impair federated learning in multi-center omics studies

    Dec 8, 2024Yuliya Burankova, Julian Klemm, Jens J. G. Lohmann +5Batch Effect CorrectionSecure Multi-Party Computation

    Institute for Computational Systems Biomedicine, University of Hamburg, 22761 Hamburg, Germany · Chair of Proteomics and Bioanalytics, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany · Department of Mathematics and Computer Science, University of Southern Denmark, 5230 Odense, Denmark +3

  18. Nonlinear Equilibrium Transitions in a Potential Game Model for Federated Learning

    Nov 18, 2024Kang Liu, Ziqi Wang, Enrique ZuazuaNash EquilibriumGame Theory

    Institut de Mathématiques de Bourgogne, Université Bourgogne Europe, CNRS, Dijon, 21000, France · Chair for Dynamics, Control, Machine Learning and Numerics – Alexander von Humboldt Professorship, Department of Mathematics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Cauerstrasse 11, Erlangen, 91058, Germany · Chair of Computational Mathematics, Fundación Deusto, Avenida de las Universidades, 24, Bilbao, 48007, Basque Country, Spain +1

  19. Local Superior Soups: A Catalyst for Model Merging in Cross-Silo Federated Learning

    Oct 31, 2024Minghui Chen, Meirui Jiang, Xin Zhang +3Communication-Efficient Distributed TrainingModel Merging

    University of British Columbia · Vector Institute · Chinese University of Hong Kong +1

  20. Trust-free Personalized Decentralized Learning

    Oct 15, 2024Yawen Li, Yan Li, Junping Du +3Decentralized LearningBlockchain-Based Federated Learning

    Beijing University of Posts and Telecommunications, Beijing, China

  21. Influence-Oriented Personalized Federated Learning

    Oct 4, 2024Yue Tan, Guodong Long, Jing Jiang +1Federated Learning AggregationNon-IID Federated Learning

    Griffith University · University of Technology Sydney · Hong Kong Polytechnic University

  22. Mitigating Data Reconstruction Attacks in Federated Learning: Conditions, Bounds, and Theoretical Guarantees

    Jul 23, 2024Xiaojin Zhang, Mingcong Xu, Wei Chen +1Federated LearningTraining Data Reconstruction Attacks

    Huazhong University of Science and Technology

  23. Tackling Feature-Classifier Mismatch in Federated Learning via Prompt-Driven Feature Transformation

    Jul 23, 2024Xinghao Wu, Xuefeng Liu, Jianwei Niu +4Prompt LearningFederated Learning

    School of Computer Science and Engineering, Beihang University, Beijing, China · Zhongguancun Laboratory, Beijing, China · Sichuan University, Sichuan, China +1

  24. Federated Class-Incremental Learning with Hierarchical Generative Prototypes

    Jun 4, 2024Riccardo Salami, Pietro Buzzega, Matteo Mosconi +2Class-Incremental LearningPrototype-Based Classification

    AImageLab - University of Modena and Reggio Emilia, Modena, Italy

  25. Personalized Additive Modeling for Multi-level Federated Learning

    May 26, 2024Shutong Chen, Guodong Long, Tianyi Zhou +3Non-IID Federated LearningFederated Learning

    University of Technology Sydney, Australia · Mohamed bin Zayed University of Artificial Intelligence, UAE · The Hong Kong Polytechnic University, China

  26. Federated Graph Learning for EV Charging Demand Forecasting with Personalization Against Cyberattacks

    Apr 30, 2024Yi Li, Renyou Xie, Chaojie Li +2Byzantine-Robust Federated LearningElectric Vehicle Charging

    College of Electronic and Information Engineering, Southwest University · Department of Electrical Engineering, City University of Hong Kong, Hong Kong · Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong

  27. PPFedIT: Towards Privacy-Preserving Federated Instruction Tuning with Few-shot Local Examples

    Mar 10, 2024Zhuo Zhang, Jingyuan Zhang, Jintao Huang +6Privacy Leakage in Language ModelsFew-Shot Learning

    Harbin Institute of Technology, Shenzhen, China · Peng Cheng Lab, Shenzhen, China · Kuaishou, Beijing, China +1

  28. FedReview: Review and Dispose Poisoned Updates without Validation Datasets or Historic Knowledge

    Feb 26, 2024Tianhang Zheng, Yanlu Li, Bohan Deng +1Federated Learning AggregationByzantine-Robust Federated Learning

    University of Missouri-Kansas City · University of Toronto

  29. Convergence Analysis of Sequential Federated Learning on Heterogeneous Data

    Nov 6, 2023Yipeng Li, Xinchen LyuNon-IID Federated LearningStochastic Optimization Convergence

    National Engineering Research Center for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing, 100876, China

  30. Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification

    Oct 6, 2023Shanshan Han, Wenxuan Wu, Baturalp Buyukates +4Byzantine-Robust Federated LearningAdversarial Attacks

    University of California, Irvine · Texas A&M University · University of Birmingham +3